Strong-tracking self-adaptive-SQKF-based SOC estimation method of emergency lamp battery

Abstract

The invention relates to a strong-tracking self-adaptive-square-root-quadrature-Kalman-filter (SQKF)-based state of charge (SOC) estimation method of an emergency lamp battery. The method comprises: establishing a second-order RC equivalent model of an emergency lamp battery; on the basis of the battery equivalent model, establishing a corresponding discrete state space model equation; and carrying out filtering estimation on the SOC value of the battery by the strong-tracking self-adaptive SQKF method. Compared with the existing SOC estimation method under the Kalman filtering framework, the provided method has high estimation precision. Meanwhile, with introduction of a time-varying fading factor and an on-line estimation system noise variance, a filter divergence problem caused by unknown time varying of the noise statistical characteristic during battery system modeling can be effectively suppressed.

Description

technical field [0001] The invention relates to the technical field of lithium batteries, in particular to a method for estimating the SOC of an emergency light battery based on strong tracking adaptive SQKF. Background technique [0002] With the rapid development of my country's economic construction, urban buildings are becoming more and more dense, and the population is relatively concentrated, which increases the danger of fire. Fire emergency lights can guide trapped people to evacuate or launch fire fighting and rescue operations in case of fire, which can greatly reduce the losses caused by fire. The widespread use of emergency lights has objectively caused difficulties in the management of emergency lights, especially in the management of emergency lights batteries. The battery's state of charge (State of Charge, SOC) provides battery usage information and battery life, so accurate estimation of battery SOC is the core and key of battery management. [0003] At pr...

AI Technical Summary

Problems solved by technology

In the ampere-hour integration method, if there is an error between the current measurement and the initial value, the error will be accumulated and magnified; although the open circuit voltage method is simple and easy to implement, the dynamic response is slow; the neural network method is susceptible to interference and requires a large amount of training data from similar batteries; The Kalman filter method has a strong correction effect on the initial error of the SOC, but requires accurate modeling of the battery model

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